检索结果-内蒙古大学图书馆

26th European Conference on Artificial Intelligence, ECAI 2023

作者： Ghiotti, Michele Manzella, Federico Pagliarini, Giovanni Sciavicco, Guido Stan, Ionel Eduard Aclai Laboratory Department of Mathematics and Computer Science University of Ferrara Italy Department of Mathematical Physical and Computer Sciences University of Parma Italy Dbs Group Faculty of Engineering Free University of Bozen-Bolzano Italy

ISBN: (纸本)9781643684369

Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees;unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e.g., temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems. © 2023 The Authors.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization 24

Coarse-to-Fine Proposal Refinement Framework for Audio Tempo...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Junyan Lu, Wei Luo, Xiangyang Yang, Rui Wang, Qian Cao, Xiaochun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

ISBN: (纸本)9798400706868

Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification purpose and fail to perform meaningful analysis of the start and end timestamps of partial forgery segments. To address this challenge, we introduce a novel coarse-to-fine proposal refinement framework (CFPRF) that incorporates a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. Specifically, the FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. The PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN. To learn robust discriminative features, we devise a difference-aware feature learning (DAFL) module guided by contrastive representation learning to enlarge the sensitive differences between different frames induced by minor manipulations. We further design a boundary-aware feature enhancement (BAFE) module to capture the contextual information of multiple transition boundaries and guide the interaction between boundary information and temporal features via a cross-attention mechanism. Extensive experiments show that our CFPRF achieves state-of-the-art performance on various datasets, including LAV-DF, ASVS2019PS, and HAD. © 2024 ACM.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

An implementation, evaluation and validation of a dynamic fire and conflagration risk indicator for wooden homes 15

An implementation, evaluation and validation of a dynamic fi...

引用

15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Strand, R.D. Kristensen, L.M. Department of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution of this paper is the design, performance evaluation, and validation of a software implementation of model-based dynamic fire risk prediction for wooden homes using local weather data. A key feature of the implementation is that the software architecture has been designed to support the use of different underlying cloud-based weather data services, and the integration as a service in third-party smart systems embedding fire risk predictions. The performed evaluation shows that the implementation is efficient, as weather data can be retrieved, preprocessed and fire risk predictions computed within seconds using only in the order of Kb's of memory;and accurate considering our sets of data, as the fire risks computed by the implementation have been validated against a set of in-situ measurements. © 2024 Elsevier B.V.. All rights reserved.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

A Survey on Event Tracking in Social Media Data Streams

引用

Big Data Mining and Analytics 2024年第1期7卷 217-243页

作者： Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Ocean College Jiangsu University of Science and TechnologyZhenjiang 212100China University Executive Office Edinburgh Napier UniversityEdinburghEH114BNUK

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are *** such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social *** tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real ***,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking *** this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social *** introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event ***,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream ***,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

关键词： Event Detection(ED) event propagation event evolution social networks

来源：评论

学校读者我要写书评

暂无评论

High-Performance SVD Partial Spectrum Computation 23

High-Performance SVD Partial Spectrum Computation

引用

2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Keyes, David Ltaief, Hatem Nakatsukasa, Yuji Sukkari, Dalal Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science & Technology Thuwal Saudi Arabia Mathematical Institute University of Oxford Oxford United Kingdom Innovative Computing Laboratory University of Tennessee Knoxville United States

ISBN: (纸本)9798400701092

We introduce a new singular value decomposition (SVD) solver based on the QR-based Dynamically Weighted Halley (QDWH) algorithm for computing the partial spectrum SVD (QDWHpartial-SVD) problems. By optimizing the rational function underlying the algorithms in the desired part of the spectrum only, the QDWHpartial-SVD algorithm efficiently computes a fraction (say 1 - 20%) of the leading singular values/vectors. We develop a high-performance implementation of QDWHpartial-SVD 1 on distributed-memory manycore systems and demonstrate its numerical robustness. We perform a benchmarking campaign against counterparts from the state-of-the-art numerical libraries across various matrix sizes using up to 36K MPI processes. Experimental results show performance speedups for QDWHpartial-SVD up to 6X and 2X against vendor-optimized PDGESVD from ScaLAPACK and KSVD on a Cray XC40 system using 1152 nodes based on two-socket 16-core Intel Haswell CPU, respectively. We also port our QDWHpartial-SVD software library to a system composed of 256 nodes with two-socket 64-Core AMD EPYC Milan CPU and achieve performance speedup up to 4X compared to vendor-optimized PDGESVD from ScaLAPACK. We also compare energy consumption for the two algorithms and demonstrate how QDWHpartial-SVD can further outperform PDGESVD in that regard by performing fewer memory-bound operations. © 2023 Owner/Author(s).

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Supercomputer Based Parallel DGTD Algorithm with LTS Scheme for Electrical Large Problems

Supercomputer Based Parallel DGTD Algorithm with LTS Scheme ...

引用

2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Wenhao Chen, Liang Li, Ping State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai Jiao Tong University Shanghai China University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division King Abdullah Thuwal Saudi Arabia

ISBN: (纸本)9781733509657

In this paper, a nodal discontinuous Galerkin time-domain (NDGTD) algorithm with parallel scheme is proposed to solve transient Maxwell's equations. With the aim to analyze the electromagnetic (EM) features of electrical large problems, the NDGTD is developed on the supercomputer named 'Tian-He', where the message passing interface (MPI) is resorted to facilitate the communication between different threads. In addition, to further improve the time-marching efficiency in dealing with multiscale problems, the local time stepping (LTS) technique is exploited. To benchmark the efficiency and accuracy of the proposed parallel DGTD method, the scattering property of a stealthy aircraft model is investigated. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

引用

computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

来源：评论

学校读者我要写书评

暂无评论

Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

引用

28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

A malware detection method using satisfiability modulo theory model checking for the programmable logic controller system

A malware detection method using satisfiability modulo theor...

引用

作者： Xie, Yaobin Chang, Rui Jiang, Liehui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Nowadays programmable logic controllers (PLCs) are suffering increasing cyberattacks. Attackers could reprogram PLCs to inject malware that would cause physical damages and economic losses. These PLC malwares are highly customized for the target which makes it difficult to extract a general pattern to detect them. In this article, we propose a PLC malware detection method based on model checking. Firstly, we improve the existing modeling method for PLC system by using the Satisfiability Modulo Theory (SMT) constraints to model the PLC system. We also present an algorithm that can transform the PLC program to the model. Our SMT-based model can deal with the features of the PLC system such as undetermined input signals, edge detection and so on. Secondly, we focus on malware detection and propose two methods, invariant extraction and rule design pattern, to generate detection rules. The former can extract the invariants from an original program, and the latter can lower the bar for user to design detection rules. Finally, we implement a prototype and evaluate it on three representative ICS scenarios. The evaluation result shows that our proposed method can successfully detect the malwares using four attack patterns. © 2020 John Wiley & Sons, Ltd.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：